Understanding the social determinants of child mortality in three latin american countries: an approach with machine learning

Abstract:

Objective: Evaluate the relationship between the social determinants of health (sociodemographic and health system resources) and the under-five mortality rate (TMM5). Methods: Municipal-level data was obtained from 2000 to 2019 from 9,142 municipalities in three Latin American countries: Brazil, Ecuador, and Mexico. To explore the relationship between social determinants and U5MR, we trained a Random Forest (RF) algorithm, and to assess model robustness, we also trained a Gradient Boosting Machine and a Model Generalized Additive. Finally, we present the mean square error (MSE), root mean square error (RMSE), and mean absolute deviation (MAD) and r-squared to compare the performance of the trained algorithms. Results: The most important variables to pbkp_redict the MMR5 according to the RF were illiteracy, poverty, and the Gini index according to the random forest algorithm. We found positive …

Año de publicación:

2023

Keywords:

Fuente:

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